Polarimetric synthetic aperture radar (PolSAR) has achieved a prominent position as a remote imaging method. However, PolSAR images are contaminated by speckle noise due to the coherent illumination employed during the data acquisition. This noise provides a granular aspect to the image, making its processing and analysis (such as in edge detection) hard tasks. This paper discusses seven methods for edge detection in multilook PolSAR images. In all methods, the basic idea consists in detecting transition points in the finest possible strip of data which spans two regions. The edge is contoured using the transitions points and a B-spline curve. Four stochastic distances, two differences of entropies, and the maximum likelihood criterion were used under the scaled complex Wishart distribution; the first six stem from the h-φ class of measures. The performance of the discussed detection methods was quantified and analyzed by the computational time and probability of correct edge detection, with respect to the number of looks, the backscatter matrix as a whole, the SPAN, the covariance an the spatial resolution. The detection procedures were applied to three real PolSAR images. Results provide evidence that the methods based on the Bhattacharyya distance and the difference of Shannon entropies outperform the other techniques.
This paper presents a comparison between two types of initializations for multilook polarimetric SAR image segmentation: a random partition and a sample quantile partition. These are the inputs of a stochastic expectation-maximization algorithm that uses a mixture of G 0 P distributions to describe the data. The parameters are unknown, and estimated by the moments method. The G 0 P law is able to describe different type of targets, like urban areas, vegetation and pasture. The experimental results on real PolSAR data are reported, showing that the use of G 0 P model with quantile partition inicialization provide good segmentation results with few iterations.
Agradeço a todos que, direta ou indiretamente, me apoiaram neste trabalho. Ao meu orientador, Dr. Prof. Nelson Delfino d'Àvila Mascarenhas, pela orientação e dedicaçãoà elaboração desta tese. Ao meu co-orientador, Dr. Luciano da Fontoura Costa, pela oportunidade de fazer o doutorado no IFSC. Ao colaborador, Dr. Alejandro César Frery Orgambide, pelo apoio nas pesquisas e, principalmente, pela amizade. Aos meus pais, Wiliam e Gleice, pela força, dedicação, apoio e carinho. Ao meu noivo, Anderson, pela dedicação, carinho e apoio. A minha família que me apoia em todos os momentos de minha vida. Aos meus amigos de Recife. Aos amigos que fiz durante este doutorado e que me apoiaram em todos estes anos em São Carlos: Débora,
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